More than three decades ago, Red Hat saw the potential of open source development and licenses to create better software and foster IT innovation. Thirty million lines of code later, Linux has not only developed to become the most successful open source software, as well as maintaining this position to this day. The commitment to open source principles continues, not only in the corporate business model, as it is also part of the work culture. In the company's assessment, these concepts have the same impact on artificial intelligence (AI) if done the right way, but the world of technology is divided regarding what would be the "right way"
The AI, especially the large language models (LLMs) behind generative AI, cannot be seen in the same way as an open program. Unlike the software, AI models consist mainly of numerical parameter models that determine how a model processes inputs, as well as the connection it makes between various data points. Trained model parameters are the result of a long process involving vast amounts of training data that are carefully prepared, mixed and processed
Although the model parameters are not software, in some aspects have a similar function to the code. It is easy to make the comparison that data is the source code of the model, you would be very close to him. No código aberto, the source code is commonly defined as the "preferred form" for making modifications to the software. The training data alone does not fit this function, given that its size differs and from its complicated pre-training process that results in a tenuous and indirect connection that any item from the data used in training has with the trained parameters and the resulting behavior of the model
Most of the improvements and enhancements in AI models that are currently happening in the community do not involve access to or manipulation of the original training data. Instead, they are the result of modifications to the model parameters or in a process or adjustment that can also be used to fine-tune the model's performance. The freedom to make these improvements to the model requires that the parameters be released with all the permissions that users receive under open source licenses
Red Hat's vision for open source AI
Red Hat believes that the foundation of open source AI lies in theopen source licensed model parameters combined with open source software components. This is a starting point for open source AI, but not the final destination of philosophy. Red Hat encourages the open source community, regulatory authorities and the industry continuing to strive for greater transparency and alignment with open source development principles when training and fine-tuning AI models
This is Red Hat's vision as a company, that encompasses an open source software ecosystem, you can engage practically with open source AI. It is not an attempt at a formal definition, as to theOpen Source Initiative(OSI) is developing with itsDefinição de IA de Código Aberto(OSAID). This is the corporation's point of view that makes open source AI feasible and accessible for the largest set of communities, organizations and suppliers
This point of view in practice is put into practice through work with open source communities, highlighted by the projectInstructLab, led by Red Hat and the effort with IBM Researchin the Granite family of licensed open source models. InstructLab significantly reduces the barriers for people who are not data scientists to contribute to AI models. With InstructLab, domain specialists from all sectors can add their skills and knowledge, both for internal use and to help a shared and widely accessible open source AI model for upstream communities
The Granite 3 model family.0 deals with a wide range of AI use cases, from code generation to natural language processing to extractinsightsof large datasets, everything under a permissive open source license. We helped IBM Research bring the Granite code model family to the open source world and continue to support the model family, both from an open source perspective and as part of our Red Hat AI offering
The repercussion of therecent announcements from DeepSeekshow how open source innovation can impact AI, both at the model level and beyond. There are obviously concerns about the approach of the Chinese platform, mainly that the model license does not explain how it was produced, what reinforces the need for transparency. That said, the mentioned disruption reinforces Red Hat's vision of the future of AI: an open future, focused on smaller models, optimized and open, that can be customized for specific business data use cases anywhere in the hybrid cloud.
Expanding AI models beyond open source
Red Hat's work in the open source AI space goes far beyond InstructLab and the Granite model family, going to the tools and platforms necessary to actually consume and productively use AI. The company has become very active in promoting technology projects and communities, such as (but not limited to)
● RamaLama, an open source project aimed at facilitating the management and local availability of AI models
● TrustyAI, an open source toolkit for building more responsible AI workflows
● Climatik, a project focused on helping to make AI more sustainable when it comes to energy consumption
● Podman AI Lab, a developer toolkit focused on facilitating experimentation with open source LLMs
Therecent announcementabout Neural Magic broadens the corporate view on AI, making it possible for organizations to align smaller and optimized AI models, including licensed open source system, with your data, wherever they live in the hybrid cloud. IT organizations can, значить, use the inference servervLLMto drive the decisions and the production of these models, helping to build an AI stack based on transparent and supported technologies
For the corporation, open source AI lives and breathes in the hybrid cloud. The hybrid cloud provides the necessary flexibility to choose the best environment for each AI workload, optimizing performance, cost, scale and security requirements. The platforms, goals and organization of Red Hat support these efforts, together with industry partners, clients and the open source community, as open source in artificial intelligence is driven
There is immense potential to expand this open collaboration in the AI space. Red Hat envisions a future that encompasses transparent work in models, just like your training. Whether next week or next month (or even earlier, given the speed of AI evolution, the company is an open community, as a whole, they will continue to support and adopt efforts to democratize and open up the world of AI
